Jumps and Information Flow in Financial Markets
Suzanne Lee* (Georgia Institute of Technology)
Abstract
We investigate the dynamics and predictability of stochastic jump arrivals
in asset prices. Specifically, we first introduce a new two-stage semi-parametric
jump predictor test to determine the informational covariates that can
affect jump occurrences up to the intra-day levels. Then, we examine the
jump dynamics from high-frequency transaction prices of individual stocks
in the Dow Jones Industrial Average. We find that the jump comes irregularly
and the size distributions are highly skewed to the left and have high
excess kurtosis. Jumps occur in the morning when there is corporate-specific
news release, and opening prices do not necessarily include jumps. We
also find that the evidence of jump clustering in normal trading: the
likelihood of future jump arrivals becomes higher when there are jumps
in previous trading hours.
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